A method for devolatilization of PC-ABS alloy material

Abstract

This application provides a devolatilization process for PC and ABS alloy materials, comprising: obtaining the initial processing temperature, preset shear rate, and initial residence time of the melt in a devolatilization device; calculating the estimated adhesion thickness and theoretical surface renewal rate of the melt on the inner wall of the device based on the initial processing temperature and the preset shear rate; if the theoretical surface renewal rate is lower than a preset threshold, adjusting the geometric parameters of the devolatilization device cavity to enhance melt flow; determining the melt residence time window based on the optimized parameters and the initial processing temperature; if the initial processing temperature is higher than a preset threshold, introducing an auxiliary medium to promote volatile migration; obtaining the final volatile concentration and material performance data, comparing them with preset indicators, and determining the compliance of the process parameters.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a devolatilization process for PC and ABS alloy materials. Background Technology

[0002] In high-end manufacturing sectors such as automotive interiors and electronic enclosures, alloys of polycarbonate (PC) and acrylonitrile butadiene styrene copolymer (ABS) are widely used due to their excellent comprehensive performance, and their quality directly affects the safety and user experience of end products. With increasingly stringent environmental regulations and lightweight design requirements, the market has placed almost contradictory high standards on this material regarding low volatile organic compound content and high flowability, making innovation in material preparation processes particularly crucial.

[0003] Current mainstream material production methods have significant limitations in addressing the aforementioned dual challenges. These methods typically rely on increasing processing temperatures or adding small-molecule additives to improve flowability, but high temperatures exacerbate the decomposition of residual monomers and additives within the resin, generating more small-molecule volatiles. Furthermore, conventional vacuum devolatilization designs suffer from inefficiencies in removing these volatiles from the melt surface due to insufficient adhesion and renewal of the melt on the equipment's inner walls. This static nature of the process makes it difficult to effectively capture and remove volatiles from within the material.

[0004] This involves two closely related core technical factors. First, the surface renewal capacity of the melt during the devolatilization process determines the efficiency with which volatiles migrate from the material's interior to the surface and are removed. Insufficient renewal results in a large amount of volatiles remaining inside the melt. Second, there is the resulting contradiction between incomplete devolatilization and damage to material properties. Extending the high-temperature treatment time or increasing shear in pursuit of more thorough devolatilization can lead to excessive degradation of polymer molecular chains. This can not only generate new harmful small molecules but also directly damage the material's mechanical properties, particularly impact toughness, resulting in a trade-off between high fluidity and high toughness.

[0005] In the specific production process, the key issue is how to significantly improve the surface renewal efficiency of the melt through process methods while ensuring that the impact strength of the material does not decrease, so as to completely remove the volatile substances that are difficult to be discharged from the inside. This is to simultaneously meet the requirements of low odor, high environmental protection standards and complex thin-walled product processing. Summary of the Invention

[0006] This invention provides a devolatilization process for PC and ABS alloy materials, mainly comprising:

[0007] The process involves obtaining the initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment for the alloy material. Based on the initial processing temperature and preset shear rate, the estimated adhesion thickness of the melt on the inner wall of the equipment and the theoretical surface renewal rate are calculated. If the theoretical surface renewal rate is lower than a preset threshold, the geometric parameters of the devolatilization equipment cavity are adjusted to enhance melt flow. Using the adjusted geometric parameters and the initial processing temperature, the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow are simulated and calculated. It is determined whether the actual surface renewal rate reaches the target value and whether the internal volatile concentration distribution conforms to a preset trend. If not, the geometric parameters are optimized. Based on the optimized parameters and the initial processing temperature, the melt residence time window is determined. If the initial processing temperature is higher than a preset threshold, an auxiliary medium is introduced to promote volatile migration. The final volatile concentration and material performance data are obtained and compared with preset indicators to determine the compliance of the process parameters. Furthermore, obtaining the initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment for the alloy material includes: obtaining melt flow behavior data from the blending characteristics of the alloy material; determining the influence of shear force by combining thermal stability requirements with interfacial tension to obtain devolatilization process control parameters; for the devolatilization process control parameters, using parameter initialization settings, obtaining the processing window range from material compatibility and blending ratio to determine the volatile matter removal efficiency; based on the volatile matter removal efficiency, obtaining a rate preset mechanism from the temperature gradient distribution by combining equipment adaptability and vacuum control; judging the alloy phase separation monitoring results through the rate preset mechanism; if the alloy phase separation monitoring results meet the melt viscosity adjustment requirements, then obtaining the initial processing temperature, the preset shear rate, and the initial residence time of the melt in the devolatilization equipment from the rate preset mechanism. Furthermore, the calculation of the estimated adhesion thickness and theoretical surface renewal rate of the melt on the inner wall of the equipment includes: obtaining the zero-shear viscosity and flow activation energy from the melt rheological property database based on the initial processing temperature and the preset shear rate; using a non-Newtonian fluid model, taking the zero-shear viscosity, the flow activation energy, and the preset shear rate as inputs, and determining the power-law exponent and characteristic relaxation time through nonlinear fitting; combining the equipment geometric configuration data and the wall slip coefficient, solving the momentum conservation and energy equations using the finite volume method to obtain the temperature field distribution and shear rate field data; extracting the normal gradient information of the inner wall of the equipment from the shear rate field data, and calculating the estimated adhesion thickness based on boundary layer theory; deriving the tangential velocity distribution in the near-wall region based on the shear rate field data, and using a vortex renewal model to calculate the average renewal frequency of surface units based on the flow stability criterion, thereby obtaining the theoretical surface renewal rate.Furthermore, adjusting the geometric parameters of the devolatilization equipment cavity to enhance melt flow includes: obtaining a comparison result between the theoretical surface renewal rate and a preset renewal rate threshold; if the theoretical surface renewal rate is lower than the preset renewal rate threshold, triggering a geometric adjustment command; extracting the initial flow channel cross-sectional shape and size parameters from the equipment's three-dimensional model according to the geometric adjustment command; introducing a periodic perturbation term into the initial flow channel cross-sectional profile function using a parametric modeling method to generate candidate flow channel cross-sectional profiles; arranging the candidate flow channel cross-sectional profiles along the flow direction to construct a new geometric model; simulating the melt flow within the new geometric model using computational fluid dynamics methods to extract streamline distribution and velocity gradient tensor fields; evaluating the tensile and folding flow-induced intensity based on the streamline distribution and the velocity gradient tensor field; verifying the structural stress field through finite element analysis to determine the final adjustment scheme. Furthermore, the simulation calculation of the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow includes: querying the melt property database based on the adjusted geometric parameters and the initial processing temperature to obtain the melt viscosity value and volatile diffusion coefficient value; establishing a three-dimensional mesh model of the flow simulation domain using the geometric parameters and assigning the melt property parameters; setting boundary conditions and calculating the velocity vector field and concentration scalar field using a transient solver; extracting surface element sets from the walls of the flow simulation domain, statistically analyzing the residence time distribution based on the velocity vector field data, calculating the local surface renewal rate, and summarizing to obtain the actual surface renewal rate distribution; extracting volatile concentration spatial data from the concentration scalar field and calculating the concentration gradient field and mass transfer flux value; and determining the internal volatile concentration distribution trend through cross-sectional concentration variation coefficient analysis. Furthermore, optimizing the geometric parameters includes: comparing the actual surface renewal rate distribution with a preset target threshold, and extracting the radial concentration data sequence of the internal volatile concentration distribution; calculating the gradient value of the concentration data sequence and determining whether it exhibits a monotonically decreasing trend; if the actual surface renewal rate does not reach the preset target threshold or the gradient value does not exhibit a monotonically decreasing trend, then setting a periodic flow channel parameter adjustment range; calculating the wall shear stress field for multiple parameter combinations and re-acquiring the actual surface renewal rate distribution; establishing a parameter-renewal rate response relationship model based on multiple sets of actual surface renewal rate distribution results; searching for the optimal parameter combination through the response relationship model, updating the simulation settings, and extracting the optimized actual surface renewal rate distribution and the internal volatile concentration distribution for verification.Furthermore, determining the melt residence time window includes: obtaining the optimized geometric parameters and the initial processing temperature as boundary conditions, inputting the flow and heat transfer coupled simulation, and calculating the shear stress field and temperature spatiotemporal distribution field within the equipment channel; using the infinitesimal element tracking method, releasing tracer particles, recording their trajectories, and extracting the shear stress field values, temperature values, and residence time spectra experienced by each particle; calculating the cumulative degradation amount of each particle along the time integral according to the degradation kinetic model to obtain the predicted value of the molecular chain degradation degree; comparing the cumulative degradation amount with the preset critical degradation degree to screen the set of effective time points; and determining the lower and upper boundaries of the time window from the set of effective time points to obtain the melt residence time window. Furthermore, the introduction of an auxiliary medium to promote volatile migration includes: acquiring the residence time window and the initial processing temperature, and determining whether the initial processing temperature is higher than a preset threshold; if the initial processing temperature is higher than the preset threshold, determining the inert gas injection amount through the temperature difference ratio; generating a physical devolatilization medium using a microbubble expansion mechanism based on the inert gas injection amount, and acquiring the internal dynamics of the melt during the gas rupture process; setting the medium introduction timing based on the internal dynamics of the melt, adjusting the melt viscosity, and determining the volatile migration path towards the surface; extracting migration promotion process data based on the volatile migration path, and determining the optimal window for volatile migration to the surface; acquiring melt outlet data and material performance data after cooling and granulation, and determining the final volatile concentration and impact toughness test results.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0009] The problem of low volatile removal efficiency and easy molecular chain degradation caused by insufficient melt surface renewal is addressed by this method. First, the estimated adhesion thickness and theoretical surface renewal rate are calculated based on the initial processing parameters. If the theoretical value is lower than the threshold, the cavity structure of the devolatilization equipment is adjusted to introduce periodic flow channels to enhance the stretching and folding flow inside the melt, thereby improving the actual surface renewal rate and forming a volatile concentration gradient from the inside to the outside. Then, within the residence time window that meets the requirements for molecular chain degradation, if the processing temperature is too high, inert gas microbubbles are introduced as a physical devolatilization medium. Their expansion and rupture promote the migration of volatiles. Finally, by monitoring the outlet volatile concentration and material impact toughness, it is determined whether the process parameter set meets the environmental protection and processing performance indicators, thus achieving the dual effect of efficient devolatilization and material performance protection. Attached Figure Description

[0010] Figure 1 This is a flowchart of a devolatilization process for PC and ABS alloy materials according to the present invention.

[0011] Figure 2This is a schematic diagram of the module framework of S103 in the devolatilization process of PC and ABS alloy materials according to the present invention;

[0012] Figure 3 This is a schematic diagram of the module framework of S105 in the devolatilization process of PC and ABS alloy materials according to the present invention.

[0013] Figure 4 This is a schematic diagram of the module framework of S106 in the devolatilization process of PC and ABS alloy materials according to the present invention;

[0014] Figure 5 This is a schematic diagram of the module framework of S107 in the devolatilization process of PC and ABS alloy materials according to the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0016] like Figures 1-5 The devolatilization process of PC and ABS alloy materials in this embodiment may specifically include:

[0017] S101. Obtain the initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment for the polycarbonate-acrylonitrile-butadiene-styrene copolymer alloy material.

[0018] Melt flow behavior is obtained from alloy blending characteristics, and the influence of shear force is determined by combining thermal stability requirements with interfacial tension to obtain devolatilization process control. For this devolatilization process control, parameter initialization settings are used, and the processing window range is obtained from material compatibility and blending ratio to determine the volatile matter removal efficiency. When the volatile matter removal efficiency is determined, a rate preset mechanism is obtained from temperature gradient distribution through equipment adaptability and vacuum control to assess alloy phase separation monitoring. If the alloy phase separation monitoring meets the melt viscosity adjustment criteria, the initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment are obtained from the rate preset mechanism.

[0019] The initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment for polycarbonate-acrylonitrile-butadiene-styrene copolymer alloy materials can be obtained through various methods.

[0020] Specifically, the determination of these parameters requires comprehensive consideration of the alloy material's composition, thermal properties, rheological characteristics, and devolatilization process requirements. In one embodiment, the initial processing temperature is obtained based on thermal property test data of the alloy material.

[0021] For example, the glass transition temperature and melting temperature range of the PC / ABS alloy can be obtained by differential scanning calorimetry.

[0022] It should be noted that the initial processing temperature is usually set within a range of 10 to 40 degrees Celsius above the peak melting temperature of the alloy to ensure that the material is fully melted and to avoid overheating and degradation.

[0023] For example, for a common PC / ABS alloy, whose peak melting temperature is tested to be 230 degrees Celsius, the initial processing temperature can be set between 240 and 260 degrees Celsius.

[0024] Preferably, the processing guidelines provided by the material supplier should be consulted, and fine-tuning should be performed based on preliminary plasticization experiments with a small amount of material. The preset shear rate is closely related to the rheological behavior of the material.

[0025] It is understandable that the shear rate affects the melt viscosity and the shear heat generated during processing.

[0026] In one embodiment, the apparent viscosity of the PC / ABS alloy at different temperatures and shear rates is tested using a capillary rheometer, and rheological curves are plotted. The preset shear rate can be selected within the typical operating range of commonly used processing equipment (such as a twin-screw extruder), for example, 100 to 1000 seconds to the power of -1, and preferably a rate value that puts the melt in the "shear-thinning" region and has a suitable viscosity.

[0027] For example, according to rheological curves, at 250 degrees Celsius, when the shear rate is approximately the negative first power of 500 seconds, the melt viscosity is in a stable range suitable for mixing and conveying, and this range can be preset as the processing shear rate. Regarding the initial residence time of the melt in the devolatilization equipment, its determination needs to consider the type of devolatilization equipment, operating conditions, and the content of volatiles to be removed. Devolatilization equipment typically refers to the section in an extruder or dedicated devolatilization unit used for vacuum removal of small molecule volatiles.

[0028] Specifically, the initial residence time can be obtained through a combination of theoretical calculations and empirical estimations. One possible approach is to estimate the average residence time based on the geometric volume of that section of the devolatilization equipment and the volumetric flow rate of the melt in that section.

[0029] For example, for the vacuum devolatilization section of a twin-screw extruder, with a cavity volume of V and a melt volumetric flow rate of Q, the theoretical average residence time t can be estimated as V divided by Q. Furthermore, considering devolatilization efficiency, the initial residence time is typically set to 1.2 to 2 times the theoretically calculated value to ensure that volatiles have sufficient time to diffuse to the melt surface and be removed.

[0030] Preferably, the initial residence time can also be verified and corrected by monitoring the residual volatile matter in the granules after devolatilization.

[0031] S102. Based on the initial processing temperature and preset shear rate, calculate the estimated adhesion thickness of the melt on the inner wall of the equipment and the theoretical surface renewal rate.

[0032] Based on the initial processing temperature and preset shear rate, the zero-shear viscosity and flow activation energy are obtained from the melt rheological property database. Using the Carreau-Yasuda model, with the zero-shear viscosity, flow activation energy, and preset shear rate as inputs, the power-law exponent and characteristic relaxation time of the non-Newtonian fluid model are determined through nonlinear fitting. Using the non-Newtonian fluid model, combined with the three-dimensional dimensional data of the equipment geometry and a preset wall slip coefficient, the momentum conservation and energy equations are solved using the finite volume method to obtain the spatial data of the temperature field distribution and the shear rate field. The gradient information of the normal to the inner wall of the equipment is extracted from the shear rate field data. Based on the velocity percentage definition in boundary layer theory, the normal distance at which the velocity reaches a specific percentage threshold of the main flow field is calculated to obtain the estimated adhesion thickness of the melt on the inner wall of the equipment. Based on the flow channel cross-sectional shape in the equipment geometry, the tangential velocity distribution in the near-wall region is derived from the shear rate field. By excluding backflow regions using flow stability criteria, a vortex renewal model is employed. Based on the tangential velocity distribution and channel characteristic dimensions, the average renewal frequency of surface elements is calculated to obtain the theoretical surface renewal rate. Sensitivity analysis is performed on the comparison between mesh refinement and coarsening for the proposed computational mesh generation scheme. If the relative deviation between the estimated adhesion thickness and the theoretical surface renewal rate under different mesh densities is less than a preset convergence threshold, then the estimated adhesion thickness and the theoretical surface renewal rate are determined to be the final calculation results.

[0033] In one implementation, the estimated adhesion thickness of the melt on the inner wall of the equipment is calculated primarily based on the effect of the initial processing temperature and the preset shear rate on the melt viscosity.

[0034] Specifically, melt viscosity is a key factor affecting adhesion thickness, which can be estimated using known rheological models.

[0035] It should be noted that the initial processing temperature determines the thermal state of the melt, while the preset shear rate affects its flow behavior. This applies to the extrusion processing of PC / ABS alloy materials.

[0036] For example, when the initial processing temperature is set to 250 degrees Celsius and the preset shear rate is 500 s⁻¹, the melt viscosity can be obtained from the rheological curve, thus allowing the adhesion thickness to be calculated. One possible approach is to use boundary layer theory, treating the adhesion thickness as a retention layer between the melt and the inner wall, whose thickness is directly proportional to viscosity and inversely proportional to shear rate. This method ensures that the calculation results are applicable to the inner wall conditions of a twin-screw extruder. Furthermore, the calculation process for estimating the adhesion thickness must consider the non-Newtonian fluid characteristics of the melt.

[0037] For example, for PC / ABS alloys, the melt exhibits shear thinning behavior at high temperatures, which means that a higher shear rate reduces viscosity, thereby thinning the adhesion layer.

[0038] Understandably, this calculation helps predict the risk of equipment blockage.

[0039] Specifically, the base value is first derived from the temperature-dependent viscosity function, and then multiplied by a correction factor for the shear rate.

[0040] For example, in one scenario, if an increase in temperature causes a 10% decrease in viscosity, the adhesion thickness will decrease accordingly, helping to optimize processing parameters to maintain uniform flow. This process emphasizes the interaction between parameters to ensure the accuracy of calculations.

[0041] Preferably, the calculation of the theoretical surface renewal rate is based on the adhesion thickness, reflecting the frequency of melt surface renewal.

[0042] In one embodiment, the surface renewal rate is defined as the ratio of the melt surface to new melt per unit time, and is directly related to the shear rate.

[0043] Specifically, this rate value is obtained by calculating the ratio of melt flow rate to adhesion thickness.

[0044] For example, in the application of PC / ABS alloys in devolatilization equipment, when the shear rate increases, the surface renewal rate increases, which promotes the discharge of volatiles.

[0045] It should be noted that this calculation can be verified by simulating the flow field to ensure that the melt surface remains active during actual processing.

[0046] In one embodiment, the theoretical surface renewal rate also needs to take into account the influence of the initial processing temperature and the heat transfer factor.

[0047] For example, at high temperatures, the melt fluidity increases, and the turnover rate increases accordingly.

[0048] Specifically, the effect of temperature on flow rate is first estimated, and then multiplied by the shear rate to obtain the final value. This is used in the continuous extrusion process of PC / ABS alloys.

[0049] For example, the refresh rate can increase by 15% for every 10-degree Celsius increase in temperature, which helps improve devolatilization efficiency. This method provides a reliable prediction tool through parameter linkage. Furthermore, to verify the calculation results, the parameters can be adjusted experimentally in practical operation.

[0050] It should be noted that the calculation processes for the estimated adhesion thickness and the theoretical surface renewal rate are interrelated, with the former being the basis and the latter an extension.

[0051] In one possible implementation, the adhesion thickness is first calculated to be 0.5 mm, and then the surface renewal rate is derived from this to be 2 times per second. This correlation ensures optimization of overall processing parameters, applicable to different batches of PC / ABS alloy materials. In one embodiment, the calculation process can be extended to the analysis of multiple sections of the equipment's inner wall.

[0052] Specifically, for the vacuum section of the devolatilization equipment, the adhesion thickness needs to take into account the influence of vacuum level, while the surface renewal rate is related to the section length.

[0053] For example, if the roughness of the inner wall of the equipment increases, the adhesion thickness increases slightly, but this can be compensated for by increasing the shear rate. This extension demonstrates the versatility of the calculation, maintaining a consistent melt state in PC / ABS alloy processing.

[0054] Preferably, the entire computing framework emphasizes a data-driven approach.

[0055] Understandably, historical processing data is used to calibrate model parameters to ensure the accuracy of the estimated values.

[0056] For example, in one scenario, the temperature coefficient is adjusted based on the average value of multiple experiments, making the adhesion thickness calculation more realistic. This framework supports the parameter calculation requirements of the claims and provides a flexible implementation path.

[0057] In one embodiment, the output of the theoretical surface renewal rate can be used for subsequent process monitoring.

[0058] Specifically, alarm thresholds are set through calculations to avoid volatile residues caused by low renewal rates. This is used in the production of PC / ABS alloy granules.

[0059] For example, when the renewal rate falls below a threshold, the shear rate is adjusted to restore equilibrium. This application highlights the practical value of computation. Furthermore, the technical goal of calculating melt adhesion thickness and surface renewal rate is to improve processing stability. Specifically, this process reduces equipment maintenance requirements and improves the uniformity of the final product's quality.

[0060] In one possible implementation, by optimizing these parameters, the devolatilization efficiency of the PC / ABS alloy is enhanced, and the residual volatile matter content is reduced to an acceptable level.

[0061] For example, in a real-world production environment, the combined calculation of initial processing temperature and preset shear rate can be integrated into the control system. Specifically, the system takes parameters into account in real time and outputs adhesion thickness and update rate values ​​for dynamic adjustment. This integration ensures the continuous application of the calculations in PC / ABS alloy processing.

[0062] S103. If the theoretical surface renewal rate is lower than the preset threshold, adjust the geometric parameters of the devolatilization equipment cavity and introduce a periodically changing flow channel cross section to enhance the stretching and folding flow inside the melt.

[0063] The calculation result of the theoretical surface renewal rate and the preset renewal rate threshold are obtained. If the theoretical surface renewal rate is lower than the preset threshold, a geometric structure adjustment command is triggered. The command includes an adjustment target of enhancing the tensile and folding flow inside the melt. According to the geometric structure adjustment command, the initial flow channel cross-sectional shape and size parameters are extracted from the three-dimensional model of the equipment. Using a parametric modeling method, a sinusoidal periodic perturbation term is introduced into the contour function of the initial flow channel cross-section. By adjusting the amplitude and frequency of the sinusoidal periodic perturbation term, a set of candidate flow channel cross-sectional contours with periodic concave and convex features is generated. The candidate flow channel cross-sectional contours are arranged and lofted along the flow direction to construct a new three-dimensional geometric model of the devolatilization equipment cavity. Computational fluid dynamics is used to simulate the melt flow within the new geometric model. The streamline distribution and velocity gradient tensor field are extracted from the simulation results. Based on the streamline distribution, the variation characteristics of streamline curvature are calculated. Based on the velocity gradient tensor field, the tensile rate component and the rotational rate component are decomposed and quantized. By combining the stretching rate component and streamline curvature characteristics, the induced strength of the periodic flow channel on melt stretching and folding flow is evaluated. Using finite element analysis, pressure and temperature loads under operating conditions are applied to the new geometric model to simulate the structural stress field. If the induced strength meets the enhancement requirements and the maximum stress value in the structural stress field is lower than the allowable stress of the material, then the structural parameters of the new geometric model are determined as the final adjustment scheme.

[0064] In one implementation, when the theoretical surface renewal rate is lower than a preset threshold, the current geometric parameters of the devolatilization equipment cavity are first evaluated.

[0065] Specifically, the assessment includes measuring the cross-sectional shape and dimensions of the flow channels inside the cavity.

[0066] For example, in the extrusion processing of PC / ABS alloy materials, the cavity typically uses a circular or rectangular cross-section as its basis. By comparing the refresh rate with a threshold, such as a threshold set at once per second, it is determined whether adjustments are needed to introduce periodic variations. Furthermore, adjusting the geometric parameters of the devolatilization equipment cavity involves modifying the design of the flow channel cross-section.

[0067] It should be noted that periodically changing flow channel cross-sections refer to repetitive variations in the shape or size of the cross-section along the length of the cavity.

[0068] For example, the shape gradually changes from circular to elliptical and repeats cyclically. This variation is designed to influence the melt flow path, ensuring that the melt undergoes alternating compression and expansion during the continuous extrusion of PC / ABS alloys. One possible implementation is to introduce these periodic variations through machining or the addition of modules.

[0069] Specifically, in the vacuum section of the devolatilization equipment, the flow channel cross-section is adjusted to make it narrow or widen in a wave-like manner at certain intervals.

[0070] For example, the transition from a cross-section of 20 mm to 15 mm in diameter is repeated every 10 cm. This helps to create tensile flow inside the melt, where melt particles are elongated, promoting internal mixing.

[0071] For example, in the application of PC / ABS alloys in a twin-screw extruder, if the turnover rate is below a threshold, an adjustable bushing can be embedded in the cavity to achieve cross-sectional changes.

[0072] Preferably, the bushing is designed with a spiral structure, which causes the melt to fold and flow axially, that is, the melt is layered and overlapped, enhancing internal convection.

[0073] In one embodiment, the introduction of periodically varying flow channel cross-sections must take into account the non-Newtonian properties of the melt.

[0074] Specifically, the PC / ABS alloy melt thins under shear, so the change in cross-section amplifies the difference in flow velocity, resulting in enhanced stretching and folding.

[0075] For example, when the processing temperature is 240 degrees Celsius, the cross-section is adjusted every 5 centimeters to ensure uniform distribution of the melt.

[0076] Understandably, this adjustment process can be implemented in steps, first simulating the flow field to verify the effect of the changes, and then actually modifying the equipment. Furthermore, in another implementation, the amplitude of the periodic changes can be fine-tuned according to viscosity differences for different batches of PC / ABS alloys; for example, higher viscosity melts can use more drastic cross-sectional variations to maintain flow stability.

[0077] In one embodiment, enhanced stretching and folding flow within the melt is achieved by controlling the melt velocity gradient through cross-sectional changes.

[0078] Specifically, stretching flow refers to the melt being stretched at widening sections, while folding flow involves lamination at narrowing sections. For example, in devolatilization equipment, this mechanism ensures continuous renewal of the melt surface without relying solely on increasing the shear rate.

[0079] Preferably, the entire adjustment frame is suitable for multi-segment designs of devolatilization equipment. In one scenario, different cyclic cross-sectional changes are introduced for the feed section and the vacuum section to adapt to the gradual change in the melt state, thereby achieving continuous optimization in PC / ABS alloy processing.

[0080] S104. Using the adjusted equipment structure parameters and the initial processing temperature, simulate and calculate the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow.

[0081] Obtain the adjusted equipment structural parameters and the initial processing temperature. Based on the initial processing temperature, query the melt viscosity-temperature relationship database and the diffusion coefficient-temperature relationship database to obtain the melt viscosity value and volatile diffusion coefficient value at the corresponding temperature, and construct the melt property field. Using the equipment structural parameters, establish a three-dimensional mesh model of the flow simulation domain in computational fluid dynamics software. Assign the melt property field parameters to the flow simulation domain. Set the melt volumetric flow rate at the inlet boundary of the flow simulation domain, set the pressure condition at the outlet boundary, and set the no-slip boundary condition and the initial processing temperature at the cavity wall. Start the transient solver containing momentum conservation, mass conservation, and component transport equations to calculate the flow simulation domain, and obtain the velocity vector field and concentration scalar field in the steady state. Extract all mesh surface elements in contact with the melt from the wall of the flow simulation domain to form the surface element set. For each element in the surface element set, the velocity vector field data at its location is acquired. The residence time distribution is statistically analyzed by tracking the melt element trajectory, and the residence time probability density function of the melt element at that element is calculated. Based on the residence time probability density function and the volatile diffusion coefficient, the local surface renewal rate of each surface element is calculated using the time integral-based renewal rate calculation formula in surface renewal theory, and the actual surface renewal rate distribution is obtained by summing these formulas. Spatial data of volatile concentration within the entire flow simulation domain are extracted from the concentration scalar field. The partial derivatives of the concentration scalar field in the three coordinate directions are calculated to obtain the concentration gradient field. Based on the concentration gradient field and the volatile diffusion coefficient, Fick's law is used to calculate the mass transfer flux value at each point within the domain, and the volatile migration intensity is quantified using the mass transfer flux value. A series of cross-sections perpendicular to the flow direction are selected within the flow simulation domain. At each cross-section, the standard deviation and mean of the concentration scalar field data are statistically analyzed, and the concentration variation coefficient of that cross-section is obtained by dividing the standard deviation by the mean. Analyze the variation trend of the concentration variation coefficient along the flow direction for all cross-sections. If the variation trend shows a monotonically decreasing trend, it is determined that the internal volatile concentration distribution tends to be uniform. Based on the actual surface renewal rate distribution, the mass transfer flux value, and the variation trend of the concentration variation coefficient, obtain the quantitative results of the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow.

[0082] In one implementation, the adjusted equipment structure parameters are first used to perform simulation calculations in conjunction with the initial processing temperature.

[0083] Specifically, for the extrusion processing of PC / ABS alloy materials, the initial processing temperature is set to the range of 230 to 250 degrees Celsius, and the melt flow behavior is evaluated using numerical simulation software.

[0084] It should be noted that the adjusted equipment structural parameters include periodic variations in the flow channel cross-section, for example, the cross-sectional dimensions along the length of the cavity gradually change from 18 mm to 22 mm. This parameter integration aims to simulate melt dynamics under enhanced flow, ensuring that the calculations cover actual production conditions. Furthermore, simulating the actual surface renewal rate of the melt under enhanced flow involves flow field analysis.

[0085] For example, in a twin-screw extruder, a model is constructed based on the finite volume method, and the adjusted geometric parameters and initial temperature are input to calculate the update frequency of the melt surface per unit time.

[0086] Specifically, the surface renewal rate is obtained by tracking the trajectory of melt particles. For example, in the vacuum section, after the melt undergoes compression and expansion, the renewal rate can reach more than 1.5 times per second.

[0087] Understandably, this calculation process takes into account the viscosity changes of the melt, and the initial temperature affects the flow resistance, thus affecting the accuracy of the renewal rate.

[0088] Preferably, the simulation calculation of the internal volatile concentration distribution is achieved by coupling mass transfer equations.

[0089] In one possible implementation, a volatile diffusion model is combined with a flow model, and after inputting adjustment parameters, the concentration gradient distribution is simulated.

[0090] For example, in the case of PC / ABS alloys, volatiles such as residual monomers migrate from high-concentration areas within the melt to the surface, with the concentration distribution showing a higher concentration in the central region than at the surface. This distribution assessment helps verify the effectiveness of equipment adjustments, ensuring uniform removal of volatiles.

[0091] In one embodiment, the simulation process is executed step by step. First, a three-dimensional geometric model is established based on adjusted parameters, and then initial temperature boundary conditions are applied. Further, the flow equations are solved iteratively, outputting surface renewal rate and concentration distribution maps.

[0092] Specifically, for different initial temperatures, such as 240 degrees Celsius, the simulation shows a 20% increase in update rate and a more uniform concentration distribution. This method is applicable to continuous extrusion scenarios, demonstrating the versatility of the technology in alloy processing.

[0093] In one embodiment, simulation parameters are fine-tuned to accommodate viscosity differences in batch-varying PC / ABS alloys.

[0094] For example, in high-viscosity melts, enhanced flow leads to more significant changes in concentration gradient, and calculations show that the volatile matter removal efficiency is improved.

[0095] It should be noted that the entire simulation framework does not rely on specific software, but emphasizes precise parameter input to achieve an objective assessment of melt behavior. Furthermore, in the multi-stage design of the devolatilization unit, the simulation calculations cover the feed section to the vacuum section.

[0096] For example, by incorporating the initial temperature, the differences in renewal rates across different sections are assessed; for instance, the feed section has a lower renewal rate, while the vacuum section is strengthened by adjusting parameters. This segmented simulation ensures overall distribution optimization and provides reliable data support in PC / ABS alloy processing.

[0097] Understandably, the technical objective of this simulation is to verify the impact of equipment adjustments on flow enhancement, thereby reducing volatile residues in actual production. Specifically, by comparing simulation results with experimental data, the correlation between surface renewal rate and concentration distribution is confirmed; for example, a higher renewal rate results in a more uniform concentration distribution, leading to more stable alloy quality. In another implementation, for simulations under varying temperature conditions, the initial processing temperature is gradually increased, and the melt response is calculated.

[0098] Preferably, this approach incorporates sensitivity analysis to assess the impact of parameter changes on the update rate. For example, for every 5 degrees Celsius increase in temperature, the update rate increases by 0.2 times per second, thereby expanding the application scope of the technical solution.

[0099] S105. Determine whether the actual surface renewal rate reaches the preset target and whether the internal volatile concentration shows a decreasing gradient from the inside to the outside. If not, further optimize the amplitude and frequency parameters of the periodic flow channel.

[0100] Obtain the actual surface renewal rate distribution and the internal volatile concentration distribution. Compare the actual surface renewal rate distribution with a preset target threshold to determine whether the actual surface renewal rate reaches the preset target threshold. Extract the radial concentration data sequence from the internal volatile concentration distribution. Calculate the gradient value of the concentration data sequence and determine whether the gradient value exhibits a monotonically decreasing trend from the melt interior to the surface. If the actual surface renewal rate does not reach the preset target threshold or the gradient value does not exhibit the monotonically decreasing trend, perform parameter optimization. Set an initial adjustment range for the periodic flow channel amplitude and frequency. Select multiple sets of amplitude and frequency parameter combinations within the adjustment range. For each set of parameter combinations, calculate its influence on the wall shear stress distribution in the flow simulation to obtain the updated wall shear stress field. Based on the updated wall shear stress field and the volatile diffusion coefficient value, recalculate the actual surface renewal rate distribution. Based on the recalculated results of multiple sets of actual surface renewal rate distributions, establish a response relationship model between amplitude, frequency parameters, and the actual surface renewal rate. Using the aforementioned response relationship model, guided by the goal of the actual surface renewal rate approaching the preset target threshold, an optimal combination of amplitude and frequency parameters is searched. The flow simulation settings are updated using this optimal parameter combination, and the final simulation calculation is performed. The optimized actual surface renewal rate distribution and the internal volatile concentration distribution are extracted from the final simulation results. The optimized actual surface renewal rate distribution is then compared with the preset target threshold, and the radial gradient value of the optimized concentration distribution is calculated. Finally, it is determined whether the optimized actual surface renewal rate reaches the preset target threshold and whether the concentration radial gradient exhibits a monotonically decreasing trend.

[0101] In one implementation, the actual surface renewal rate is determined based on the results of the aforementioned simulation calculations.

[0102] Specifically, the surface renewal rate refers to the frequency at which the surface layer of the melt is replaced by new melt per unit time during the flow process. For example, in the extrusion processing of PC / ABS alloys, this value is output by numerical simulation software and compared with the preset target.

[0103] It should be noted that the preset target can be set to more than 1.2 times per second. If the actual value is lower than this threshold, it indicates insufficient flow enhancement. Furthermore, it is also necessary to evaluate whether the internal volatile concentration distribution exhibits a decreasing gradient from the inside to the outside. This gradient means that the volatile concentration gradually decreases from the center of the melt to the surface, promoting uniform removal.

[0104] For example, when processing PC / ABS alloys in a twin-screw extruder, the concentration distribution is analyzed using a three-dimensional spectrum generated by a simulation model.

[0105] Understandably, the decreasing gradient is achieved by calculating the concentration gradient vector, meaning the concentration value decreases layer by layer from the center of the melt cross-section to the surface; for example, the concentration at the center is 0.5% while the surface is close to 0.1%. If the distribution does not show a decreasing pattern, such as the appearance of localized high-concentration areas, it is judged that the requirements have not been met. This judgment process, combined with the initial processing temperature, such as 240 degrees Celsius, ensures that the evaluation covers actual production conditions.

[0106] Preferably, if the judgment result shows that the preset target has not been achieved, the amplitude parameters of the periodic flow channel are further optimized.

[0107] Specifically, amplitude refers to the range of change in the cross-sectional dimensions of the flow channel. For example, if the initial setting is a gradual change from 18 mm to 22 mm, the amplitude can be increased to 20 mm to 24 mm if the renewal rate is insufficient. This optimization is performed through iterative simulation, first adjusting the parameters input to the model, and then recalculating the surface renewal rate and concentration distribution.

[0108] In one embodiment, for high-viscosity PC / ABS alloys, amplitude optimization results in a more intense compressive expansion of the melt, leading to an increased surface renewal rate of 1.8 times per second and a more pronounced concentration gradient. Further optimization includes adjusting the frequency parameter, i.e., the periodic repetition rate of the flow channel cross-section changes.

[0109] For example, in the vacuum section, the cycle of change per meter increases from 5 to 8 times, promoting the migration of volatiles from the inside of the melt to the surface.

[0110] It should be noted that this frequency adjustment is based on flow field analysis and aims to enhance the shear force distribution, thereby causing the concentration distribution to decrease uniformly from the inside out.

[0111] In one possible implementation, for batch-variant alloy materials, parameters are iteratively optimized multiple times in combination with an initial temperature of 230 degrees Celsius until the target is determined to be met.

[0112] Specifically, in a continuous extrusion scenario, the optimization process is implemented step by step as follows: first, extract simulation data and calculate the deviation between the update rate and the target; then, fine-tune the amplitude and frequency according to the magnitude of the deviation, such as increasing the amplitude when the deviation is greater than 10%.

[0113] For example, in devolatilization equipment for PC / ABS alloys, this method ensures improved volatile matter removal efficiency and optimized concentration distribution, resulting in more stable alloy product quality.

[0114] Understandably, the entire process does not rely on specific software, but emphasizes precise iteration of parameters to achieve objective improvements in melt behavior. In another implementation, for PC / ABS processing under varying temperature conditions, if the target is not met after assessment, parameters can be optimized by combining temperature sensitivity analysis.

[0115] For example, when the initial temperature is raised to 250 degrees Celsius, and the frequency is adjusted to 10 times per meter, the simulation shows that the update rate reaches the preset 1.5 times per second, and the concentration exhibits a clear decreasing gradient. This approach expands the application of the technology in alloy extrusion, ensuring the versatility of flow strengthening.

[0116] S106. Based on the optimized equipment parameters and processing temperature when the target surface renewal rate is achieved, determine a melt residence time window. This window must satisfy the requirement that the predicted value of the molecular chain degradation degree is lower than the critical threshold under the preset shear strength.

[0117] The solidification values ​​and processing temperature of the parameters are obtained and input as boundary conditions into the flow and heat transfer coupled simulation to calculate the shear stress field and temperature spatiotemporal distribution field within the equipment flow channel. In the flow and heat transfer coupled simulation, the micro-element tracking method is employed. This method simulates the motion path of micro-units of the melt, releases a large number of tracer particles, and records the complete motion trajectory of each particle from the inlet to the outlet. From the trajectory data, the local shear stress field values, local temperature values, and the total residence time spectrum experienced by each particle are extracted. According to the degradation kinetic model, which includes the correlation between shear stress and temperature and the molecular chain breakage rate constant, the cumulative degradation amount is calculated by integrating along the time for the experience data sequence of each tracer particle, obtaining the predicted value of the final molecular chain degradation degree corresponding to each particle. The cumulative degradation amount of each particle is compared with the critical degradation degree. If the predicted degradation value of the particle is lower than the critical degradation degree, the residence time corresponding to that particle is marked as a valid time point. All valid time points are selected to form a set of valid time points. From the set of effective time points, find the minimum and maximum time values, and determine these two values ​​as the lower and upper boundaries of the time window solution, respectively. Any dwell time within the time window solution can ensure that, under the parameter solidification value, the predicted value of the molecular chain degradation degree is lower than the critical degradation degree.

[0118] In one implementation, the melt residence time window is determined based on the aforementioned optimized equipment parameters and processing temperature.

[0119] Specifically, the process first extracts the amplitude, frequency, and processing temperature values ​​required to achieve the target surface renewal rate. For example, in PC / ABS alloy extrusion, the optimized amplitude is 20 to 24 mm, the frequency is 8 times per meter, and the temperature is 240 degrees Celsius. These parameters are used as inputs to calculate the melt flow time range within the extruder.

[0120] It should be noted that the melt residence time window refers to the time interval from when the melt enters the vacuum section to when it leaves, ensuring the removal of volatiles while avoiding excessive exposure that could lead to degradation. Furthermore, the melt residence time window must meet the conditions under a preset shear strength.

[0121] For example, the preset shear strength is set to 100 sec reversals per second, based on the viscosity characteristics of the alloy. The degree of molecular chain degradation is predicted using a flow simulation model, which involves assessing the probability of chain breakage in the melt under shear force.

[0122] In one embodiment, for high-viscosity PC / ABS alloys, simulation calculations showed that the predicted degradation rate was 0.3% within a residence time window of 30 to 45 seconds, which is below the critical threshold of 0.5%.

[0123] Understandably, this prediction is achieved through a thermal degradation kinetic model that considers the effects of temperature and shear on the molecular chain, but does not involve specific numerical iterations.

[0124] Preferably, the prediction process for the degree of molecular chain degradation includes several key steps. First, optimized parameters and temperature are input into the simulation environment to generate the melt flow field distribution. Then, based on the flow field, the local shear strength is calculated, and combined with the molecular weight distribution of the alloy, the chain breakage rate is estimated.

[0125] Specifically, in a twin-screw extruder, the melt undergoes compression and expansion, with the shear strength distribution gradually changing from the center to the surface, causing degradation to occur primarily in the high-shear zone. By integrating these distributions, an overall degradation prediction is obtained. This process ensures that the predicted value remains within a safe range when the window is determined, promoting stable alloy product quality.

[0126] In one possible implementation, if the initial predicted value is close to the threshold, the lower limit of the time window can be fine-tuned, for example, from 30 seconds to 35 seconds, and the degradation rate can be re-simulated and verified to have decreased to 0.2%. Furthermore, this method can be extended to applications under varying temperature conditions.

[0127] For example, at a processing temperature of 230 degrees Celsius, based on an optimized frequency of 10 times per meter, the residence time window was determined to be 25 to 40 seconds. Simulations showed that under a preset shear strength of 80 seconds per second, the predicted molecular chain degradation was 0.4%, meeting the threshold requirement.

[0128] It should be noted that this adjustment takes into account the effect of temperature on viscosity, ensuring that the window adapts to variations in different batches of alloy. In another implementation, for continuous extrusion scenarios, the window determination is combined with real-time monitoring data.

[0129] Specifically, after extracting the simulation results, the deviation between the degradation prediction and the threshold is calculated. If the deviation is less than 5%, the window is confirmed to be valid.

[0130] For example, in devolatilization equipment for PC / ABS alloys, a window of 35 to 50 seconds is set, predicting a degradation rate of 0.25%, which is below the 0.5% threshold. This method achieves precise control over melt behavior through iterative verification.

[0131] For example, in batch-variable alloy processing, temperature sensitivity analysis is preferably used to refine the window. Based on optimized parameters of 240 degrees Celsius, degradation curves are simulated at different residence times, with a selected window of 32 to 48 seconds, ensuring that the predicted value remains below 0.35% at a shear strength of 120 seconds in reverse time.

[0132] Understandably, this analytical process assesses the sensitivity of temperature changes to degradation rates, thereby expanding the versatility of the technology in alloy extrusion.

[0133] S107. Within a defined residence time window, if the processing temperature is higher than the critical temperature for additive decomposition, inert gas microbubbles are introduced as a physical devolatilization medium. The expansion and rupture of the gas in the melt promotes the migration of volatiles to the surface.

[0134] The residence time window and processing temperature threshold are obtained. Based on the temperature condition, if the processing temperature is higher than the additive decomposition temperature, the inert gas injection amount is determined by the temperature difference ratio. From the inert gas injection amount, a physical devolatilization medium is generated using a microbubble expansion mechanism. This mechanism is based on the expansion force generated by gas volume change, obtaining the internal dynamics of the melt during the gas rupture process. Based on the internal dynamics of the melt, the timing of medium introduction is introduced. This timing is set according to the peak value of dynamic fluctuations, adjusting the melt viscosity, and determining the migration path of volatiles towards the melt surface. Based on the surface migration of the melt, migration promotion process data is obtained. This data is extracted from the path transfer trajectory to determine the optimal window for volatile migration to the surface.

[0135] In one implementation, based on a defined melt residence time window, when the processing temperature exceeds the critical temperature for additive decomposition, inert gas microbubbles are introduced as a physical devolatilization medium.

[0136] Specifically, the critical temperature refers to the threshold at which additive molecules begin to thermally decompose; for example, in PC / ABS alloy processing, a typical value is 250 degrees Celsius.

[0137] It should be noted that this introduction aims to avoid the degradation of additives at high temperatures, which could affect alloy quality. Microbubbles formed by inert gases such as nitrogen are used to enhance devolatilization efficiency. The process first monitors the processing temperature; if it exceeds a critical value, gas is injected into the vacuum section of the extruder to form microbubbles with diameters ranging from 10 to 50 micrometers. These microbubbles are uniformly distributed in the melt, promoting the physical separation of volatiles. Furthermore, the inert gas microbubbles promote the migration of volatiles to the surface through expansion and rupture.

[0138] For example, in melt flow, microbubbles expand when heated, increasing in volume and causing an increase in internal pressure, subsequently bursting and releasing gas. This expansion process creates localized turbulence, enhancing the mixing effect inside the melt, thereby accelerating the diffusion and migration of volatiles such as solvent residues from deep within the melt to the surface.

[0139] Understandably, this mechanism is based on the principle of gas-liquid interfacial tension, where the shock wave generated during rupture further disturbs the melt and reduces the residence time of volatiles.

[0140] In one possible implementation, for high-viscosity PC / ABS alloys, the microbubble injection rate is controlled at 0.5 to 1 liter per minute to ensure that expansion and rupture occur within the residence time window, such as 35 to 45 seconds, to avoid excessive disturbance that could lead to melt instability.

[0141] Preferably, the method is applied under different processing conditions, such as when the temperature is 260 degrees Celsius, argon microbubbles are introduced in combination with optimized equipment parameters.

[0142] Specifically, gas is injected into the melt through a specialized nozzle, and the expansion process is accelerated in a vacuum environment. After rupture, volatiles escape from the surface with the gas. This method can be extended to continuous extrusion scenarios to ensure improved volatile removal rates while maintaining molecular chain integrity.

[0143] In one embodiment, for batch-variant alloys, the microbubble size was adjusted to 20 micrometers, and simulations showed improved migration efficiency and reduced volatile residue to below 0.2%. In another embodiment, if the residence time window is 40 to 55 seconds, carbon dioxide is introduced as an inert gas to form microbubbles that promote migration.

[0144] It should be noted that the expansion of carbon dioxide microbubbles is driven by temperature, and the collapse of the bubbles when they burst enhances surface renewal and promotes the rapid escape of volatiles.

[0145] For example, in twin-screw extruders, this process is combined with shear strength, and the microbubble distribution gradually changes from the center to the edge, resulting in highly efficient migration without additional degradation. Through the application of this physical medium, the technical solution adapts to various alloy processing temperature variations, ensuring the stability and versatility of the devolatilization process.

[0146] S108. Monitor and acquire the final concentration of volatiles at the melt outlet and the impact toughness test data of the material after cooling and granulation, after the introduction of inert gas microbubbles.

[0147] After inert gas injection, melt outlet data is acquired. An initial volatile concentration is determined based on the expansion force generated by gas volume change using a microbubble expansion mechanism. From this initial value, a temperature threshold is assessed. If the temperature exceeds a preset threshold, the melt viscosity is adjusted to obtain the volatile migration path based on dynamic fluctuation peaks. Using this migration path, surface transfer is optimized using a gas injection ratio based on the temperature difference ratio to determine the final volatile concentration. Based on this final concentration, a cooling granulation process is performed to obtain material performance data based on the melt surface transfer trajectory. Using this material performance data, the impact toughness test results are analyzed to determine the correspondence between the final volatile concentration and the impact toughness test data, based on the migration promotion process data.

[0148] In one embodiment, the volatile concentration of the melt after the introduction of inert gas microbubbles is monitored.

[0149] Specifically, an online infrared spectrometer is installed near the exit of the extruder die. This spectrometer uses a fiber optic probe aligned with the steadily flowing melt stream to collect the infrared absorption spectrum of the melt surface in real time.

[0150] It should be noted that specific absorption peaks in infrared spectra are associated with the chemical bond vibrations of target volatile molecules (such as residual solvents, oligomers, or small molecule products from the decomposition of auxiliaries).

[0151] For example, for monitoring benzene series residues, the characteristic peak of hydrocarbon stretching vibrations with wavenumbers around 3030 cm⁻¹ can be focused on. The analyzer's built-in algorithm, based on a pre-established calibration model, converts the intensity of the absorption spectrum into the mass percentage concentration of volatiles, thereby achieving continuous online acquisition of the final concentration of volatiles at the melt outlet. Furthermore, to obtain impact toughness data of the material after cooling and granulation, the particles produced by the process need to be standardized for sample preparation and testing. Particles collected from the stable process stage are dried and then injection molded into specimens conforming to testing standards, such as notched cantilever beam impact specimens conforming to ISO 180 standards, using an injection molding machine at standard temperature and pressure.

[0152] Preferably, the injection molding process parameters need to match the processing characteristics of the alloy material to ensure that the internal structure of the sample is similar to that of the actual product, and to avoid introducing additional performance deviations due to secondary processing.

[0153] Understandably, impact toughness testing is performed on a standardized impact testing machine. The specific procedure involves placing the prepared notched specimen horizontally on the machine's support, with the notch facing away from the impact pendulum. The pendulum is released, allowing it to fall freely and impact the specimen. The instrument automatically records the energy absorbed at specimen fracture. This energy value, divided by the original cross-sectional area at the notch, yields the material's cantilever beam impact strength, expressed in kilojoules per square meter. This data directly reflects the material's toughness under high-speed impact loads.

[0154] In one possible implementation, to establish the correlation between process parameters and performance, the monitoring system binds the volatile concentration data acquired online with the particle batches produced within the corresponding time period.

[0155] For example, when the infrared spectrometer detects that the concentration of volatiles in the outlet melt is consistently below 0.15%, the system labels the particles produced during that period as "Batch A". Subsequently, the particles of "Batch A" are collected separately and subjected to sample preparation and impact testing according to the aforementioned method. In this way, the specific impact toughness value corresponding to a certain devolatilization level of the material can be clearly obtained under specific microbubble introduction process conditions.

[0156] Preferably, the types of volatile substances targeted for online monitoring can be adjusted for different PC / ABS alloy formulations.

[0157] For example, for alloys containing specific flame retardants, the possible thermal decomposition product is hydrogen bromide. In this case, the online analyzer needs to be equipped with a detection module targeting the characteristic absorption peak of hydrogen bromide. Simultaneously, the sample preparation temperature for impact testing also needs to be adjusted accordingly to simulate the processing conditions of the material in actual applications. Through the above specific monitoring and testing implementation methods, key process indicators and final material performance data can be systematically acquired and correlated.

[0158] S109. Compare the final volatile matter concentration and impact toughness data with the preset environmental standard indicators and the minimum toughness indicators required for thin-walled processability to obtain the compliance judgment results of the process parameter set.

[0159] Impact toughness assessment data is obtained from volatile concentration comparisons. Temperature thresholds are used to determine thin-wall processing requirements for compliance with environmental standards. Based on these thin-wall processing requirements, a minimum toughness threshold is obtained, and the migration path is adjusted according to the gas injection ratio to obtain a set of process parameter adjustments. From this set of process parameter adjustments, surface transfer ratios are used to assess material performance data against compliance thresholds, determining the volatile migration simulation path under the adjusted injection gas ratio. Based on this volatile migration simulation path, the compliance determination result of the process parameter set is obtained by adjusting the migration path based on preset environmental standard indicators.

[0160] In one implementation, the final volatile matter concentration and impact toughness data are compared with preset environmental protection standard indicators.

[0161] Specifically, the preset environmental protection standards are based on relevant regulations, such as the upper limit for volatile organic compound (VOC) residues in PC / ABS alloy materials, where the concentration of benzene compounds must not exceed 0.2%. The system software module inputs the VOC concentration values ​​obtained from online monitoring into a comparison function. This function uses a threshold comparison method; if the monitored concentration is lower than or equal to the preset upper limit, it is determined to meet environmental protection requirements.

[0162] It should be noted that this comparison process can be integrated into the process control system to ensure real-time feedback. Furthermore, the impact toughness data is compared with the minimum toughness index required for thin-wall processability. Thin-wall processability indexes typically refer to the impact resistance requirements of materials in thin-wall injection molding, such as a minimum impact strength of 15 kJ / m², to ensure the durability of the product in thin-walled structures.

[0163] In one possible implementation, the impact toughness value obtained from the test is matched with the index using a numerical comparison algorithm. If the toughness value is higher than or equal to the minimum requirement, it is considered to meet the processability conditions.

[0164] Understandably, this comparison can be achieved through automated scripts, which automatically output the results after inputting data.

[0165] For example, to obtain the compliance determination result of the process parameter set, the above two comparison results are logically ANDed. Specifically, when the volatile matter concentration meets the environmental protection standards and the impact toughness meets the minimum index, the system determines that the entire process parameter set is compliant; otherwise, it is marked as non-compliant and an adjustment signal is triggered.

[0166] Preferably, in the PC / ABS alloy extrusion process, this determination can be applied to parameter sets with different microbubble introduction rates, such as the parameter set when the microbubble flow rate is 5 liters per minute, and its compliance can be confirmed by comparison.

[0167] In one embodiment, the source of the preset indicators needs further explanation. Environmental standard indicators are derived from industry regulations, such as the EU REACH regulation's restrictions on volatile organic compounds, while thin-wall processability indicators are based on material application scenarios, such as the thin-wall requirements for electronic product casings. These indicators are stored in a database, and the system calls the corresponding values ​​for numerical verification during comparison. Furthermore, this mechanism allows users to adjust the indicator thresholds according to specific alloy formulations; for example, alloys containing flame retardants can have stricter upper limits on volatile organic compounds set to adapt to different production needs.

[0168] Specifically, the comparison process includes data preprocessing steps, such as averaging the volatile concentration data to eliminate the influence of noise.

[0169] For example, the average of 10 continuously monitored data points is compared with a standard to ensure accuracy. Regarding impact toughness, data can be obtained by taking the median value from multiple spline tests and comparing it with the lowest possible index. This approach enhances the reliability of the assessment.

[0170] Preferably, in another implementation, the compliance determination result can be bound to a set of process parameters to form a report output.

[0171] For example, if a parameter set includes an extrusion temperature of 250 degrees Celsius and a microbubble introduction pressure of 2 bar, and the parameter set is compliant after comparison, then the parameter set is marked as usable for widespread use.

[0172] Understandably, this bonding helps optimize subsequent production batches. Furthermore, to demonstrate versatility, this comparative method was applied to different formulations of the PC / ABS alloy.

[0173] For example, for high-gloss alloys, the thin-walled workability index can be set to an impact strength of not less than 18 kJ per square meter, and the applicability of the process parameters can be verified by comparison.

[0174] It should be noted that this method is not limited to a single alloy type, but can be extended to the processing of similar thermoplastic materials.

[0175] In one possible implementation, the judgment result is displayed using a visual interface, such as green for compliance and red for non-compliance, to facilitate quick operator response.

[0176] For example, if the volatile concentration is 0.1 percent and the impact toughness is 20 kJ / m², the system determines compliance and records the parameter set details.

[0177] Specifically, the entire comparison logic can be implemented through conditional statements, such as: if the concentration is less than or equal to the standard and the toughness is greater than or equal to the indicator, then it is compliant. This structure ensures the objectivity and consistency of the judgment.

[0178] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for devolatilization processing of PC and ABS alloy materials, characterized in that, include: The initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization device of the alloy material are obtained. Based on the initial processing temperature and the preset shear rate, the estimated adhesion thickness of the melt on the inner wall of the equipment and the theoretical surface renewal rate are calculated; if the theoretical surface renewal rate is lower than the preset threshold, the geometric parameters of the devolatilization equipment cavity are adjusted to enhance melt flow; Using the adjusted geometric parameters and the initial processing temperature, the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow are simulated and calculated. It is then determined whether the actual surface renewal rate reaches the target value and whether the internal volatile concentration distribution conforms to a preset trend. If not, the geometric parameters are optimized. Based on the optimized parameters and the initial processing temperature, the melt residence time window is determined. If the initial processing temperature is higher than a preset threshold, an auxiliary medium is introduced to promote volatile migration. The final volatile concentration and material performance data are obtained and compared with preset indicators to determine the compliance of the process parameters.

2. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The process of obtaining the initial processing temperature, preset shear rate, and initial residence time of the melt in the devolatilization equipment for the alloy material includes: obtaining melt flow behavior data from the blending characteristics of the alloy material; determining the influence of shear force by combining thermal stability requirements with interfacial tension to obtain devolatilization process control parameters; for the devolatilization process control parameters, using parameter initialization settings, obtaining the processing window range from material compatibility and blending ratio to determine the volatile matter removal efficiency; based on the volatile matter removal efficiency, obtaining a rate preset mechanism from the temperature gradient distribution by combining equipment adaptability and vacuum control; judging the alloy phase separation monitoring results through the rate preset mechanism; if the alloy phase separation monitoring results meet the melt viscosity adjustment requirements, then obtaining the initial processing temperature, the preset shear rate, and the initial residence time of the melt in the devolatilization equipment from the rate preset mechanism.

3. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The calculation of the estimated adhesion thickness and theoretical surface renewal rate of the melt on the inner wall of the equipment includes: obtaining the zero-shear viscosity and flow activation energy from the melt rheological property database based on the initial processing temperature and the preset shear rate; using a non-Newtonian fluid model, taking the zero-shear viscosity, the flow activation energy, and the preset shear rate as inputs, and determining the power-law exponent and characteristic relaxation time through nonlinear fitting; combining the equipment geometric configuration data and the wall slip coefficient, solving the momentum conservation and energy equations using the finite volume method to obtain the temperature field distribution and shear rate field data; extracting the normal gradient information of the inner wall of the equipment from the shear rate field data, and calculating the estimated adhesion thickness according to boundary layer theory; deriving the tangential velocity distribution in the near-wall region based on the shear rate field data, and calculating the average renewal frequency of surface units using a vortex renewal model based on the flow stability criterion to obtain the theoretical surface renewal rate.

4. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The adjustment of the geometric parameters of the devolatilization equipment cavity to enhance melt flow includes: obtaining a comparison result between the theoretical surface renewal rate and a preset renewal rate threshold; if the theoretical surface renewal rate is lower than the preset renewal rate threshold, triggering a geometric adjustment command; extracting the initial flow channel cross-sectional shape and size parameters from the equipment's three-dimensional model according to the geometric adjustment command; introducing a periodic perturbation term into the initial flow channel cross-sectional profile function using a parametric modeling method to generate candidate flow channel cross-sectional profiles; arranging the candidate flow channel cross-sectional profiles along the flow direction to construct a new geometric model; simulating the melt flow within the new geometric model using computational fluid dynamics methods to extract streamline distribution and velocity gradient tensor field; evaluating the tensile and folding flow-induced intensity based on the streamline distribution and velocity gradient tensor field; and verifying the structural stress field through finite element analysis to determine the final adjustment scheme.

5. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The simulation calculation of the actual surface renewal rate and internal volatile concentration distribution of the melt under enhanced flow includes: querying the melt property database to obtain the melt viscosity and volatile diffusion coefficient values ​​based on the adjusted geometric parameters and the initial processing temperature; establishing a three-dimensional mesh model of the flow simulation domain using the geometric parameters and assigning the melt property parameters; setting boundary conditions and calculating the velocity vector field and concentration scalar field using a transient solver; extracting surface element sets from the walls of the flow simulation domain, calculating the local surface renewal rate based on the residence time distribution of the velocity vector field data, and summarizing the results to obtain the actual surface renewal rate distribution; extracting volatile concentration spatial data from the concentration scalar field and calculating the concentration gradient field and mass transfer flux values; and determining the trend of the internal volatile concentration distribution through cross-sectional concentration coefficient of variation analysis.

6. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The optimization of the geometric structure parameters includes: comparing the actual surface renewal rate distribution with a preset target threshold, and extracting the radial concentration data sequence of the internal volatile concentration distribution; calculating the gradient value of the concentration data sequence and determining whether it exhibits a monotonically decreasing trend; if the actual surface renewal rate does not reach the preset target threshold or the gradient value does not exhibit a monotonically decreasing trend, then setting a periodic flow channel parameter adjustment range; calculating the wall shear stress field for multiple parameter combinations and re-acquiring the actual surface renewal rate distribution; establishing a parameter-renewal rate response relationship model based on multiple sets of actual surface renewal rate distribution results; searching for the optimal parameter combination through the response relationship model, updating the simulation settings, and extracting the optimized actual surface renewal rate distribution and the internal volatile concentration distribution for verification.

7. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The determination of the melt residence time window includes: obtaining the optimized geometric parameters and the initial processing temperature as boundary conditions, inputting the flow and heat transfer coupled simulation, and calculating the shear stress field and temperature spatiotemporal distribution field within the equipment channel; using the infinitesimal element tracking method, releasing tracer particles, recording their motion trajectories, and extracting the shear stress field values, temperature values, and residence time spectra experienced by each particle; calculating the cumulative degradation amount of each particle along the time integral according to the degradation kinetic model to obtain the predicted value of the molecular chain degradation degree; comparing the cumulative degradation amount with the preset critical degradation degree to screen the effective time point set; determining the lower and upper boundaries of the time window from the effective time point set to obtain the melt residence time window.

8. The devolatilization process for PC and ABS alloy materials as described in claim 1, characterized in that, The process of introducing an auxiliary medium to promote volatile migration includes: acquiring the residence time window and the initial processing temperature, and determining whether the initial processing temperature is higher than a preset threshold; if the initial processing temperature is higher than the preset threshold, determining the amount of inert gas injected based on the temperature difference ratio; generating a physical devolatilization medium using a microbubble expansion mechanism based on the amount of inert gas injected, and acquiring the internal dynamics of the melt during the gas rupture process; setting the timing of medium introduction based on the internal dynamics of the melt, adjusting the melt viscosity, and determining the volatile migration path towards the surface; extracting migration promotion process data based on the volatile migration path, and determining the optimal window for volatile migration to the surface; acquiring melt outlet data and material performance data after cooling and granulation, and determining the final volatile concentration and impact toughness test results.

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